Repozytorium publikacji - Politechnika Gdańska

In this paper we consider the problem of noncausal identification of nonstationary,linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. It also allows one to account for the distribution of measurement noise, and in particular - to cope with heavy-tailed disturbances, such as Laplacian noise, or light-tailed disturbances, such as uniform noise.

Autorzy

• prof. dr hab. inż. Maciej Niedźwiecki link otwiera się w nowej karcie ,
• Szymon Gackowski